Since the word-based ASR approach is impractical for these languages due to the ever increasing vocabulary size, it is not possible to sufficiently cover the large vocabulary of words without utilizing subword units [1]}. The subword modelling technique, where each word is split into two or more morpheme-like units, decreases the out of vocabulary (OOV) rate; besides the smaller subword vocabulary reduces the model complexity significantly. It also makes it possible to build n-gram language models to cover millions of words [2]}. It still requires special solutions for the state-of-the-art neural network language models [3]} to train an output layer that has a vast number of possible output symbols. DNNs trained on several thousand hours of speech have reduced the word error rate (WER) significantly compared to the traditional methods and achieve word-level accuracy of nearly 90% for vocabulary sizes of about 200,000 for English  [4]}. Such ASR systems are now widely used for commercial and entertainment purposes.
